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Multi-objective LSTM ensemble model for household short-term load forecasting

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Abstract

With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the LSTM network in order to generate several sub-models. With the diversity and accuracy of the sub-models as the objective functions, the improved MOEA/D algorithm is then used to optimize the parameters, in order to enhance the overall performance of the sub-models and ensure their differences. Finally, a DBN-based combination strategy is used to combine the single forecasts in order to form the ensemble forecast, and reduce the adverse effects of model uncertainty and data noise on the prediction accuracy. The experimental results show that the proposed method has several advantages in prediction accuracy and generalization capacity, compared with several current intelligent prediction methods.

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Funding

The authors are very grateful to the anonymous reviewers for their valuable comments on improving this article, and EditSprings (https://www.editsprings.cn/) for the expert linguistic services provided. Additionally, this work is supported by Hunan Provincial Natural Science Foundation of China (No. 2020JJ4587), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110423), Degree & Postgraduate Education Reform Project of Hunan Province (No. 2019JGYB115), Changsha Municipal Natural Science Foundation (No. kq2014063), and Open Fund Project of Fujian Provincial Key Laboratory of Data Intensive Computing (No. BD202004).

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Authors and Affiliations

  1. College of Automation and Electronics Information, Xiangtan University, Xiangtan, 411105, China

    Chaodong Fan, Yunfan Li & Lingzhi Yi

  2. Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University, Foshan, 528000, China

    Chaodong Fan, Yunfan Li & Leyi Xiao

  3. School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China

    Chaodong Fan, Leyi Xiao & Xilong Qu

  4. College of Foreign Languages, College of Foreign Languages, Inter-Disciplinary Research Center of Language Intelligence and Cultural Heritages, Hunan University, Changsha, 410082, China

    Zhaoyang Ai

  5. Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou Normal University, Quanzhou, 362000, China

    Leyi Xiao

  6. School of Software and Information Engineering, Hunan Software Vocational and Technical University, Xiangtan, 411100, China

    Chaodong Fan

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  1. Chaodong Fan
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  2. Yunfan Li
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  4. Leyi Xiao
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Correspondence to Leyi Xiao.

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Fan, C., Li, Y., Yi, L. et al. Multi-objective LSTM ensemble model for household short-term load forecasting. Memetic Comp. 14, 115–132 (2022). https://doi.org/10.1007/s12293-022-00355-y

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  • DOI: https://doi.org/10.1007/s12293-022-00355-y

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